A scalable approach to model big and interacted queries for materialized view through data mining

摘要

With big data, the volume of the manipulated data is rapidly growing, we find several business sectors contributing to this expansion such as: (i) the social networks, (ii) the use of the sensors, (iii) the commercial transactions. To integrate this actual reality, the management of small, medium, large business sectors needs analytical applications, such as scalable data warehouse, to answer effectively big and interacted queries. This interaction is exploited in the phase of the physical design of data warehouse using optimizations structure such as the materialized view. Selecting an appropriate set of views to materialize under some resource constraints is known as view selection problem (VSP). In this paper, we propose an approach to solve VSP by profiting the world of multi-query optimization in order to generate the global execution plan integrating new dimensions Big and Interacted Queries, to ensure scalability, we use the clustering technique K-means and operations of refinement in order to capture volume of interacted queries without passing by enumeration of all logical plans of the queries and we use our plan to materialize views. Finally, experiments are conducted to show the scalability of our approach.